{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt('linear.csv', delimiter=',')\n",
    "# 画图\n",
    "plt.scatter(data[1:,0],data[1:,1]) # 第0列，第1列。\n",
    "plt.title('Age Vs Quality (Test set)')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('Quality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "3cda970e6884fb79a45ae6e9ee6abf824e496828"
   },
   "outputs": [],
   "source": [
    "# 数据拆分，test_size = 0.3，30%为测试集\n",
    "x_train, x_test, y_train, y_test = train_test_split(data[1:, 0], data[1:, 1], test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1D->2D，给数据增加一个维度，主要是训练模型的时候，函数要求传入2维的数据\n",
    "x_train = x_train[:, np.newaxis]\n",
    "x_test = x_test[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "18deccad20151df1e49555831abb8a350cc3a3e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练模型\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "ba105297c6f331ac1e88938080298924c87ddb8e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练集的散点图\n",
    "plt.scatter(x_train, y_train, color = 'b')\n",
    "# 模型对训练集的预测结果，x_train,model.predict(x_train)分别对应X轴和Y轴\n",
    "plt.plot(x_train,model.predict(x_train), color ='r' , linewidth=5)\n",
    "# 画表头和xy坐标描述\n",
    "plt.title('Age Vs Quality (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Quality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "494d34b17d3afbe6942c7449f8b95fe37854b96f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试集的散点图\n",
    "plt.scatter(x_test, y_test, color = 'b')\n",
    "# 模型对测试集的预测结果\n",
    "plt.plot(x_test,model.predict(x_test), color ='r', linewidth=5)\n",
    "# 画表头和xy坐标描述\n",
    "plt.title('Age Vs Quality (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Quality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
